Bayesian inference provides a simple and unified approach to data analysis, allowing experimenters to assign probabilities to competing hypotheses of interest, on the basis of the current state of knowledge. By incorporating relevant prior information, it can sometimes improve model parameter estimates by many orders of magnitude.

This book combines the quantitative aspects of statistics with written explanations of what the results of statistical tests mean in a way that students will understand. The book includes a student friendly system of pedagogy to ensure student success.